Persistent data structures on a dispersed storage network memory

ABSTRACT

Systems and Methods for dispersed data structures (DDS) in a distributed storage network are disclosed. A dispersed storage processing unit handling a request to insert a key value pair into a DDS could lookup what the most up to date DDS is, which could be held by a single source with a pointer to the current DDS root. The processing unit could then descend the DDS until it finds the leaf node that owns the requester&#39;s key and make a copy of the leaf with the key inserted. The processing unit could then make a copy of the parent of the node, replacing the pointer to the copied node with a pointer to the new copy, repeat this step until the root is reached, and make a copy of the root in a similar fashion but also including a pointer to the original DDS root.

CROSS REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.15/453,575, entitled “PERSISTENT DATA STRUCTURES ON A DISPERSED STORAGENETWORK MEMORY,” filed Mar. 8, 2017, which is hereby incorporated hereinby reference in its entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks, and moreparticularly to dispersed or cloud storage.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on a remote orInternet storage system. The remote or Internet storage system mayinclude a RAID (redundant array of independent disks) system and/or adispersed storage system that uses an error correction scheme to encodedata for storage.

In a RAID system, a RAID controller adds parity data to the originaldata before storing it across an array of disks. The parity data iscalculated from the original data such that the failure of a single disktypically will not result in the loss of the original data. While RAIDsystems can address certain memory device failures, these systems maysuffer from effectiveness, efficiency and security issues. For instance,as more disks are added to the array, the probability of a disk failurerises, which may increase maintenance costs. When a disk fails, forexample, it needs to be manually replaced before another disk(s) failsand the data stored in the RAID system is lost. To reduce the risk ofdata loss, data on a RAID device is often copied to one or more otherRAID devices. While this may reduce the possibility of data loss, italso raises security issues since multiple copies of data may beavailable, thereby increasing the chances of unauthorized access. Inaddition, co-location of some RAID devices may result in a risk of acomplete data loss in the event of a natural disaster, fire, powersurge/outage, etc.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) in accordance with the presentdisclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present disclosure;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present disclosure;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an example of slice naminginformation for an encoded data slice (EDS) in accordance with thepresent disclosure;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present disclosure;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present disclosure;

FIG. 9 is a schematic block diagram of an example of a dispersed storagenetwork in accordance with the present disclosure;

FIG. 10A is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10B is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10C is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10D is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10E is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10F is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10G is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure;

FIG. 10H is a schematic block diagram of an example of a dispersedstorage network in accordance with the present disclosure; and

FIG. 10I is a flowchart illustrating an example of a data storageprocess in accordance with the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofdispersed storage (DS) computing devices or processing units 12-16, a DSmanaging unit 18, a DS integrity processing unit 20, and a DSN memory22. The components of the DSN 10 are coupled to a network 24, which mayinclude one or more wireless and/or wire lined communication systems;one or more non-public intranet systems and/or public internet systems;and/or one or more local area networks (LAN) and/or wide area networks(WAN).

The DSN memory 22 includes a plurality of dispersed storage units 36 (DSunits) that may be located at geographically different sites (e.g., onein Chicago, one in Milwaukee, etc.), at a common site, or a combinationthereof. For example, if the DSN memory 22 includes eight dispersedstorage units 36, each storage unit is located at a different site. Asanother example, if the DSN memory 22 includes eight storage units 36,all eight storage units are located at the same site. As yet anotherexample, if the DSN memory 22 includes eight storage units 36, a firstpair of storage units are at a first common site, a second pair ofstorage units are at a second common site, a third pair of storage unitsare at a third common site, and a fourth pair of storage units are at afourth common site. Note that a DSN memory 22 may include more or lessthan eight storage units 36.

DS computing devices 12-16, the managing unit 18, and the integrityprocessing unit 20 include a computing core 26, and network orcommunications interfaces 30-33 which can be part of or external tocomputing core 26. DS computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the dispersed storage units 36.

Each interface 30, 32, and 33 includes software and/or hardware tosupport one or more communication links via the network 24 indirectlyand/or directly. For example, interface 30 supports a communication link(e.g., wired, wireless, direct, via a LAN, via the network 24, etc.)between computing devices 14 and 16. As another example, interface 32supports communication links (e.g., a wired connection, a wirelessconnection, a LAN connection, and/or any other type of connectionto/from the network 24) between computing devices 12 and 16 and the DSNmemory 22. As yet another example, interface 33 supports a communicationlink for each of the managing unit 18 and the integrity processing unit20 to the network 24.

In general and with respect to DS error encoded data storage andretrieval, the DSN 10 supports three primary operations: storagemanagement, data storage and retrieval. More specifically computingdevices 12 and 16 include a dispersed storage (DS) client module 34,which enables the computing device to dispersed storage error encode anddecode data (e.g., data object 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a DS computing devices 12-14. Forinstance, if a second type of computing device 14 has data 40 to storein the DSN memory 22, it sends the data 40 to the DS computing device 16via its interface 30. The interface 30 functions to mimic a conventionaloperating system (OS) file system interface (e.g., network file system(NFS), flash file system (FFS), disk file system (DFS), file transferprotocol (FTP), web-based distributed authoring and versioning (WebDAV),etc.) and/or a block memory interface (e.g., small computer systeminterface (SCSI), internet small computer system interface (iSCSI),etc.).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-16 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generateper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network operations can furtherinclude monitoring read, write and/or delete communications attempts,which attempts could be in the form of requests. Network administrationincludes monitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

To support data storage integrity verification within the DSN 10, theintegrity processing unit 20 (and/or other devices in the DSN 10 such asmanaging unit 18) may assess and perform rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. Retrieved encoded slices are assessed and checked for errorsdue to data corruption, outdated versioning, etc. If a slice includes anerror, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slicesthat are not received and/or not listed may be flagged as missingslices. Bad and/or missing slices may be subsequently rebuilt usingother retrieved encoded data slices that are deemed to be good slices inorder to produce rebuilt slices. A multi-stage decoding process may beemployed in certain circumstances to recover data even when the numberof valid encoded data slices of a set of encoded data slices is lessthan a relevant decode threshold number. The rebuilt slices may then bewritten to DSN memory 22. Note that the integrity processing unit 20 maybe a separate unit as shown, included in DSN memory 22, included in thecomputing device 16, managing unit 18, stored on a DS unit 36, and/ordistributed among multiple storage units 36.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the I0 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as I0 ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber. In the illustrated example, the value X11=aD1+bD5+cD9,X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The s lice name functions as at least part of a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

In order to recover a data segment from a decode threshold number ofencoded data slices, the computing device uses a decoding function asshown in FIG. 8. As shown, the decoding function is essentially aninverse of the encoding function of FIG. 4. The coded matrix includes adecode threshold number of rows (e.g., three in this example) and thedecoding matrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a diagram of an example of a dispersed storage network. Thedispersed storage network includes a DS (dispersed storage) clientmodule 34 (which may be in DS computing devices 12 and/or 16 of FIG. 1),a network 24, and a plurality of DS units 36-1 . . . 36-n (which may bestorage units 36 of FIG. 1 and which form at least a portion of DSmemory 22 of FIG. 1), a DSN managing unit 18, and a DS integrityverification module (not shown). The DS client module 34 includes anoutbound DS processing section 81 and an inbound DS processing section82. Each of the DS units 36-1 . . . 36-n includes a controller 86, aprocessing module 84 (e.g. computer processor) including acommunications interface for communicating over network 24 (not shown),memory 88, a DT (distributed task) execution module 90, and a DS clientmodule 34.

In an example of operation, the DS client module 34 receives data 92.The data 92 may be of any size and of any content, where, due to thesize (e.g., greater than a few Terabytes), the content (e.g., securedata, etc.), and/or concerns over security and loss of data, distributedstorage of the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DS client module 34, the outbound DS processing section 81receives the data 92. The outbound DS processing section 81 processesthe data 92 to produce slice groupings 96. As an example of suchprocessing, the outbound DS processing section 81 partitions the data 92into a plurality of data partitions. For each data partition, theoutbound DS processing section 81 dispersed storage (DS) error encodesthe data partition to produce encoded data slices and groups the encodeddata slices into a slice grouping 96.

The outbound DS processing section 81 then sends, via the network 24,the slice groupings 96 to the DS units 36-1 . . . 36-n of the DSN memory22 of FIG. 1. For example, the outbound DS processing section 81 sendsslice group 1 to DS storage unit 36-1. As another example, the outboundDS processing section 81 sends slice group #n to DS unit #n.

In one example of operation, the DS client module 34 requests retrievalof stored data within the memory of the DS units 36. In this example,the task 94 is retrieve data stored in the DSN memory 22. Accordingly,and according to one embodiment, the outbound DS processing section 81converts the task 94 into a plurality of partial tasks 98 and sends thepartial tasks 98 to the respective DS storage units 36-1 . . . 36-n.

In response to the partial task 98 of retrieving stored data, a DSstorage unit 36 identifies the corresponding encoded data slices 99 andretrieves them. For example, DS unit #1 receives partial task #1 andretrieves, in response thereto, retrieved slices #1. The DS units 36send their respective retrieved slices 99 to the inbound DS processingsection 82 via the network 24.

The inbound DS processing section 82 converts the retrieved slices 99into data 92. For example, the inbound DS processing section 82de-groups the retrieved slices 99 to produce encoded slices per datapartition. The inbound DS processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DS processing section 82 de-partitions the data partitions torecapture the data 92.

In one example of operation, the DSN of FIGS. 1 and 9 may be utilizedfor purposes of implementing a dispersed data structure as set forthbelow and in conjunction with FIGS. 10A to 10I. Note, while theseembodiments are described in the context of functionality provided by DSprocessing unit 16, this functionality may be implemented utilizing anymodule and/or unit of the dispersed storage network (DSN), alone or incombination, including but not limited to DS Unit 36, DS ProcessingIntegrity Unit 20 and/or DS Managing Unit 18.

A dispersed lockless concurrent index (DLCI), or other dispersed datastructure (DDS) on top of a DSN memory, provides an efficient mechanismto store and search for data. A DDS implemented in a manner where everyupdate produces an entirely new DDS is said to be persistent. Retainingold versions of a persistent DDS allows for enhanced functions such as:the ability to audit modification to the structure; the ability toroll-back the structure to a known good version in the event of anerror; and the ability to perform compute or other operations on aknown, unchanging version of the structure. Making an entire copy of aDDS on every update, however, could be resource intensive. Instead a DDSmay be implemented such that only sources that are impacted by theupdate are copied and all other sources are reused. For example, a DSprocessing unit 16 handling a request to insert a key value pair into aDLCI could perform the following steps.

First—the DS processing unit 16 can lookup what the most up to date, orcurrent version, DDS is (in this case the most up to date DLCI), whichinformation could be held by a single source with a pointer (rootpointer) to the current DLCI root. For example, as shown in FIG. 10A,and step 600 of FIG. 10I, DS processing unit 16 may retrieve and decodeslice 500 to determine original root node pointer 501. Alternatively,this information could be stored in multiple slices on multiple DSunits.

Second—DS processing unit 16 may descend the DLCI until it finds theleaf node that owns the requester's key, and make a copy of the leafwith the key inserted. The process is depicted in FIGS. 10B through 10E,and steps 602-610 of FIG. 10I. Specifically, as shown in FIG. 10B andstep 602 of FIG. 10I, DS processing unit 16 retrieves and receivesslices 502-1, 502-2 . . . to 502-n from the respective memories (88-1,88-2 . . . 88-n) of DS units 36-1, 36-2 . . . 36-n. These slices arethen decoded to generate original root node 503 which includes a pointerto the first internal node 504 corresponding to the leaf node sought. Asshown in FIG. 10C, and step 604 of FIG. 10I, the pointer to the firstinternal node 504 is then used to obtain the original first internalnode 508 (in this case via slices 506-1 to 506-n) which node includes apointer 510 to a next original internal node related to the leaf node,if applicable, or a pointer 512 to the original leaf node itself(original leaf node pointer). As shown in FIG. 10D and step 606 of FIG.10I, if there is another original internal node between the originalfirst internal node and the original leaf node, the DS unit wouldretrieve this next original internal node 514, which node would includeeither a pointer 510 to yet another next original internal node or apointer 512 to the original leaf node. As shown in FIG. 10I step 606,this step of finding the next original internal node leading to theoriginal leaf node could be repeated as many times as there areadditional internal nodes beyond the first internal node. Accordingly, aperson of skill in the art will appreciate that the content of “next”original internal node 514, and pointer 516, will change for eachiteration, that is to say “next” original internal node 514 and “next”original internal node pointer 516 (respective original internal nodepointer) are with respect to a given iteration. In the event that thereis not another original internal node beyond the original first internalnode, DS processing unit retrieves and decodes original leaf node 518,including original leaf node content 520, via slides 516-n to 516-n asshown in FIG. 10D and step 608 of FIG. 10I. As shown in FIG. 10E, andstep 610 of FIG. 10I, the content of the original leaf node can then bemodified to generate modified leaf node content 524, in modified leafnode 522, which node can then be stored as slices 526-1 to 526-n in DSunits 36-1 to 36-n, by DS processing unit 16.

Third—DS processing unit 16 then makes a copy of the parent of a node,starting with the parent of the modified leaf node, replacing thepointer to the original node with a pointer to the modified node; andFourth—the previous step is repeated, this time using the parent of theparent (grandparent) of the leaf node, and so on, until the originalroot node is reached. This process is shown in FIGS. 10F and 10G andsteps 612 and 614 of FIG. 10I. For example, as shown in FIG. 10F andstep 612, with respect to any internal nodes between the first internalnode and the leaf node (at this point the modified leaf node), if any,the modified internal node 528 includes a pointer 532 to the modifiedleaf node (if the parent of the leaf node), i.e. a modified leaf nodepointer, or a respective pointer 530 to the previous modified internalnode (e.g. the modified grandparent node would include a pointer to themodified parent node . . . ). DS processing unit 16 can then encode thisinformation as slices 534-1 to 534-n, which can be stored in DS units36-1 to 36-n. As shown in FIG. 10I, this step 612 could be repeated asmany times as there are additional internal nodes between the root nodeand leaf node, beyond the first internal node, in which case the pointerto the “previous” modified internal node (respective modified internalnode pointer) would change for each iteration. As shown in FIG. 10G, andstep 614 of FIG. 10I, a modified first internal node 536, includingeither a pointer 530 to the previous modified internal node, or apointer 532 to the modified leaf node (if there is only one internalnode between the root and leaf), is then encoded and stored as slices538-1 to 538-n in DS units 36-1 to 36-n.

Fifth—A copy of the original root node is then made and modified toproduce modified root node 540 including a pointer 542 to the modifiedfirst internal node. This process is shown in FIG. 10H and step 616 ofFIG. 10I. Modified root node 540 additionally includes a pointer 501 tothe original DLCI root found in the first step (the DLCI parent). Thisinformation is then encoded and stored as slices 544-1 to 544-n in DSunits 36-1 to 36-n. The DS processing unit may also choose to includemetadata about the update, such as a timestamp. Note, while in theexamples of FIGS. 10B through 10H the node information is stored asmultiple slices, with one slice per DS unit, these are merely examples.More generally speaking, this information could be stored as one or moreslice on one or more DS units, including any subset thereof.

After these steps are complete, the newly created root now contains themost up to date version of the DLCI. The DS processing unit 16 wouldonly succeed to the requester if it could make a checked update to theroot pointer, changing it to point at this newly created root. Someclasses of updates to a DDS have the property that the order in whichupdates are applied does not change the resulting structure (mergeable).If updates are mergeable, the ds processing unit may instead use anothermethod to ensure that the update is eventually merged into the rootpointer, such as a write intent. This allows concurrent updates to befully conflict free. If many roots are merged into a new root in asingle operation, the new root could have multiple DLCI parents.

To rollback to a previous version of the DLCI would only requiremodifying the root pointer. A requester can create an audit trail of theDLCI by following the DLCI parents on the roots and reconstructing thehistory of updates. DS units 36 may enforce that history is preserved bypersisting nodes to non-deletable immutable storage (WORM). In a WORMscheme, requesters may continue to remove objects from the presentlyvisible version of the data structure, but any object can be recoveredby following an audit trail. Alternatively, if it is desirable toeventually reclaim space used by a DDS a garbage collector (GC) can beimplemented that would delete old versions of the DDS according to somepolicy. Some examples of GC policy include: Time based—delete versionsafter some period of time; and Snapshotting—regularly select someversions of the DDS as snapshots and remove all other intermediateversions.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signalA has a greater magnitude than signal B, a favorable comparison may beachieved when the magnitude of signal A is greater than that of signal Bor when the magnitude of signal B is less than that of signal A. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from Figureto Figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information. A computer readable memory/storage medium,as used herein, is not to be construed as being transitory signals perse, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method of rolling back an updated disperseddata structure (DDS) in a dispersed storage network (DSN) having aplurality of dispersed storage (DS) units, wherein the DDS is stored asa plurality of encoded data slices in one or more of the plurality of DSunits, and wherein the DDS includes an original root node, at least oneoriginal internal node including an original first internal node, and anoriginal leaf node, the method comprising: retrieving one or more firstencoded data slices of the plurality of encoded data slices, the one ormore first encoded data slices containing the original root node, theoriginal root node including an original first internal node pointerrelated to the original first internal node; retrieving one or moresecond encoded data slices of the plurality of encoded data slices, theone or more second encoded data slices containing the at least oneoriginal internal node including the original first internal node andthe original leaf node; wherein the at least one original internal nodeincludes an original leaf node pointer related to the original leaf nodeand a respective original internal node pointer related to each originalinternal node beyond the original first internal node; wherein the stepof retrieving one or more second encoded data slices of the plurality ofencoded data slices is based on the original first internal nodepointer, the respective original internal node pointer related to eachoriginal internal node beyond the original first internal node and theoriginal leaf node pointer; storing a modified leaf node in the DSNbased on a first modification to the original leaf node; and storing atleast one modified internal node including a modified first internalnode in the DSN based on one or more second modifications to the atleast one original internal node including the original first internalnode; wherein the at least one modified internal node includes arespective modified internal node pointer related to each of the atleast one modified internal nodes beyond the modified first internalnode and a modified leaf node pointer related to the modified leaf node;and storing a modified root node in the DSN based on a thirdmodification to the original root node; and wherein the modified rootnode includes a modified first internal node pointer related to themodified first internal node and an original root node pointer relatedto the original root node; and rolling back to a previous version of theDDS by modifying a current root node pointer to a root node pointer of aprevious version of the DDS.
 2. The method of claim 1 further comprisescreating an audit trail of the DDS by following parent DDS root nodepointers and reconstructing a history of updated DDSs.
 3. The method ofclaim 2 further comprises the one or more of the plurality of DS unitsenforcing preservation of the history of updated DDSs by storing them tonon-deletable immutable storage.
 4. The method of claim 3, wherein, whenrequesters remove data objects from a present version of the DDS, thedata objects are recoverable by following the audit trail.
 5. The methodof claim 1 further comprises reclaiming storage space in the DSN bydeleting older versions of the DDS according to a policy.
 6. The methodof claim 5, wherein the policy includes either of: time-based deletionsafter some period of time or regularly selecting some versions of theDDS as snapshots and removing all other intermediate versions.
 7. Themethod of claim 1, further comprising determining a current version ofthe DDS prior to the step of retrieving one more first encoded dataslices of the plurality of encoded data slices.
 8. The method of claim7, wherein determining the current version includes assessing theoriginal root node pointer.
 9. The method of claim 1, further includingstoring metadata relating to the first modification, the one or moresecond modifications, and the third modification.
 10. A dispersedstorage processing unit for rolling back an updated dispersed datastructure (DDS) in a dispersed storage network (DSN) having a pluralityof dispersed storage (DS) units, wherein the DDS is stored as aplurality of encoded data slices in one or more of the plurality of DSunits, and wherein the DDS includes an original root node, at least oneoriginal internal node including an original first internal node, and anoriginal leaf node, the dispersed storage processing unit comprising: acommunications interface; a memory; and a computer processor; where thememory includes instructions for causing the computer processor to:retrieve one or more first encoded data slices of the plurality ofencoded data slices, the one or more first encoded data slicescontaining the original root node, the original root node including anoriginal first internal node pointer related to the original firstinternal node; retrieve one or more second encoded data slices of theplurality of encoded data slices based on an original first internalnode pointer, a respective original internal node pointer related toeach original internal node beyond the original first internal node andan original leaf node pointer, wherein the one or more second encodeddata slices contain the at least one original internal node includingthe original first internal node and the original leaf node and whereinthe at least one original internal node includes the original leaf nodepointer related to the original leaf node and the respective originalinternal node pointer related to each original internal node beyond theoriginal first internal node; store a modified leaf node in the DSNbased on a first modification to the original leaf node; store at leastone modified internal node including a modified first internal node inthe DSN based on one or more second modifications to the at least oneoriginal internal node including the original first internal node,wherein the at least one modified internal node includes a respectivemodified internal node pointer related to each of the at least onemodified internal nodes beyond the modified first internal node and amodified leaf node pointer related to the modified leaf node; and storea modified root node in the DSN based on a third modification to theoriginal root node, wherein the modified root node includes a modifiedfirst internal node pointer related to the modified first internal nodeand an original root node pointer related to the original root node; androll back to a previous version of the DDS by modifying a current rootnode pointer to a root node pointer of a previous version of the DDS.11. The dispersed storage processing unit of claim 10 further comprisescreating an audit trail of the DDS by following parent DDS root nodepointers and reconstructing a history of updated DDSs.
 12. The dispersedstorage processing unit of claim 11 further comprises the one or more ofthe plurality of DS units enforce preservation of the history of updatedDDSs by storage to non-deletable immutable storage.
 13. The dispersedstorage processing unit of claim 12, wherein, when requesters removedata objects from a present version of the DDS, the data objects arerecoverable by following the audit trail.
 14. The dispersed storageprocessing unit of claim 10 further comprises reclaiming storage spacein the DSN by deleting older versions of the DDS according to a policy.15. The dispersed storage processing unit of claim 14, wherein thepolicy includes any of: time-based deletions after some period of time;or regularly selecting some versions of the DDS as snapshots andremoving all other intermediate versions.
 16. A dispersed storagenetwork (DSN) for rolling back an updated dispersed data structure(DDS), comprising: a plurality of dispersed storage (DS) units, and a DSprocessing unit, wherein the DDS is stored as a plurality of encodeddata slices in one or more of the plurality of DS units, and wherein theDDS includes an original root node, at least one original internal nodeincluding an original first internal node, and an original leaf node,the dispersed storage processing unit including: a communicationsinterface; a memory; and a computer processor; where the memory includesinstructions for causing the computer processor to: retrieve one or morefirst encoded data slices of the plurality of encoded data slices, theone or more first encoded data slices containing the original root node,the original root node including an original first internal node pointerrelated to the original first internal node; retrieve one or more secondencoded data slices of the plurality of encoded data slices based on anoriginal first internal node pointer, a respective original internalnode pointer related to each original internal node beyond the originalfirst internal node and an original leaf node pointer, wherein the oneor more second encoded data slices contain the at least one originalinternal node including the original first internal node and theoriginal leaf node and wherein the at least one original internal nodeincludes the original leaf node pointer related to the original leafnode and the respective original internal node pointer related to eachoriginal internal node beyond the original first internal node; store amodified leaf node in the DSN based on a first modification to theoriginal leaf node; store at least one modified internal node includinga modified first internal node in the DSN based on one or more secondmodifications to the at least one original internal node including theoriginal first internal node, wherein the at least one modified internalnode includes a respective modified internal node pointer related toeach of the at least one modified internal nodes beyond the modifiedfirst internal node and a modified leaf node pointer related to themodified leaf node; and store a modified root node in the DSN based on athird modification to the original root node, wherein the modified rootnode includes a modified first internal node pointer related to themodified first internal node and an original root node pointer relatedto the original root node. roll back to a previous version of the DDS bymodifying a current root node pointer to a root node pointer of aprevious version of the DDS.
 17. The DSN of claim 16 further comprisescreating an audit trail of the DDS by following parent DDS root nodepointers and reconstructing a history of updated DDSs.
 18. The DSN ofclaim 17 further comprises the one or more of the plurality of DS unitsenforce preservation of the history of updated DDSs by storage tonon-deletable immutable storage.
 19. The DSN of claim 18, wherein, whenrequesters remove data objects from a present version of the DDS, thedata objects are recoverable by following the audit trail.
 20. The DSNof claim 16 further comprises reclaiming storage space in the DSN bydeleting older versions of the DDS according to a policy.